Geometric Distribution, Poisson

Click For Summary

Discussion Overview

The discussion revolves around a problem involving a geometric distribution and a Poisson distribution, specifically focusing on calculating the probability Pr(M>1) given certain conditions about their expected values and variances.

Discussion Character

  • Mathematical reasoning

Main Points Raised

  • One participant presents the problem and states the relationships between the expected values and variances of the geometric and Poisson distributions.
  • The participant believes the answer can be expressed as 1 - Pr(M=0) - Pr(M=1), which translates to 1 - e^(-lambda) - lambda*e^(-lambda), but notes the challenge of solving for lambda.
  • Another participant suggests that it is possible to solve for p and lambda using the equations λ = (1-p)/p and 2λ = (1-p)/(p^2).
  • A later reply indicates that the original poster was able to solve for lambda after seeing the response.

Areas of Agreement / Disagreement

Participants do not express disagreement, but the discussion reflects a progression from presenting a problem to finding a solution, with one participant confirming they were able to resolve the issue.

Contextual Notes

There may be limitations related to the assumptions made about the distributions and the specific values of p and lambda that were not fully explored in the discussion.

Who May Find This Useful

This discussion may be useful for individuals interested in probability theory, particularly those studying geometric and Poisson distributions and their properties.

Mad Scientists
Messages
3
Reaction score
0
The problem is the following;

N has a geometric distribution with Pr(N=0)>0. M has a Poisson distribution. You are given:

E(N) = E(M); Var(N) = 2Var(M)

Calculate Pr (M>1).

From general knowledge we know that the expected value of a variable in a geometric distribution E(N) = q/p, and Var(N) = q/(p^2).
Also; the expected value of a variable in a Poisson distribution E(M) = lambda and Var(M) also = lambda.

I believe that the answer is 1 - pr(M=0) - pr(M=1) which is the equivalent of
1-e^(-lambda)-lambda*e^(-lambda).

But this would require solving for lambda, a feat I have not yet accomplished.


Any pointers?..

Thanks in advance,

Teddy
 
Physics news on Phys.org
You should be able to solve for p and [itex]\lambda[/itex], from [itex]\lambda[/itex] = (1-p)/p, and 2[itex]\lambda[/itex] = (1-p)/(p^2).

Note that Pr(N=0) = p > 0 so [itex]\lambda < +\infty[/itex].
 
Last edited:
Thanks Enuma, I was able to solve for it.
 
NM i saw the reply.
 

Similar threads

  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 14 ·
Replies
14
Views
2K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 4 ·
Replies
4
Views
3K
Replies
8
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 1 ·
Replies
1
Views
8K
Replies
1
Views
5K
  • · Replies 3 ·
Replies
3
Views
2K